Microsemi UG0643 Image De Noising Filter User Guide
- June 9, 2024
- Microsemi
Table of Contents
Microsemi UG0643 Image De Noising Filter
Microsemi Headquarters
One Enterprise, Aliso Viejo, CA 92656 USA
- Within the USA: +1 800-713-4113
- Outside the USA: +1 949-380-6100
- Sales: +1 949-380-6136
- Fax: +1 949-215-4996
- Email: sales.support@microsemi.com
- www.microsemi.com
Microsemi makes no warranty, representation, or guarantee regarding the information contained herein or the suitability of its products and services for any particular purpose, nor does Microsemi assume any liability whatsoever arising out of the application or use of any product or circuit. The products sold hereunder and any other products sold by Microsemi have been subject to limited testing and should not be used in conjunction with mission-critical equipment or applications. Any performance specifications are believed to be reliable but are not verified, and Buyer must conduct and complete all performance and other testing of the products, alone and together with, or installed in, any end-products. Buyer shall not rely on any data and performance specifications or parameters provided by Microsemi. It is the Buyer’s responsibility to independently determine suitability of any products and to test and verify the same. The information provided by Microsemi hereunder is provided “as is, where is” and with all faults, and the entire risk associated with such information is entirely with the Buyer. Microsemi does not grant, explicitly or implicitly, to any party any patent rights, licenses, or any other IP rights, whether with regard to such information itself or anything described by such information. Information provided in this document is proprietary to Microsemi, and Microsemi reserves the right to make any changes to the information in this document or to any products and services at any time without notice.
About Microsemi
Microsemi, a wholly owned subsidiary of Microchip Technology Inc. (Nasdaq:
MCHP), offers a comprehensive portfolio of semiconductor and system solutions
for aerospace & defense, communications, data center and industrial markets.
Products include high-performance and radiation-hardened analog mixed-signal
integrated circuits, FPGAs, SoCs and ASICs; power management products; timing
and synchronization devices and precise time solutions, setting the world’s
standard for time; voice processing devices; RF solutions; discrete
components; enterprise storage and communication solutions, security
technologies and scalable anti-tamper products; Ethernet solutions; Power-
over-Ethernet ICs and midspans; as well as custom design capabilities and
services. Learn more at www.microsemi.com.
Revision History
The revision history describes the changes that were implemented in the document. The changes are listed by revision, starting with the most current publication.
Revision 3.0
The following is a summary of the changes in revision 3.0 of this document.
- Input Data_In_i is replaced with R_I, G_I and B_I to support RGB color format.
- Output Data_Out_o is replaced with R_O, G_O and B_O.
- Median Filter design logic is redesigned to support for (n x n) resolution with pipelined logics whereas previous design is implemented with Sequential FSM.
Revision 2.0
The following is a summary of the changes in revision 2.0 of this document.
- In Image De-Noising Filter Hardware Implementation, page 3:
- YCbCr in signal names was replaced with Data.
- The following text was deleted: The median filtering is only applied on the Y channel. The CB and CR signals are passed through the required pipe-lining registers to synchronize with Y channel. For the Y channel, three pixels from each of the three video lines are read into three shift-registers.
- Details about the Image De-noising Filter testbench were added. For more information, see Testbench, page 5.
- The Timing Diagrams section and the appendix were deleted.
- The number of buffers in the hardware was updated from four to five. For more information, see Image De-Noising Filter Hardware Implementation, page 3.
- Information about port widths was added. For more information, see Inputs and Outputs, page 4.
- Resource utilization data was updated. For more information, see Resource Utilization, page 10.
Revision 1.0
The first publication of this document.
Introduction
Images captured from image sensors are affected by noise. Impulse noise is the
most common type of noise, also called salt-and-pepper noise. It is caused by
malfunctioning pixels in camera sensors, faulty memory locations in the
hardware, or errors in data transmission. Image denoising plays a vital role
in digital image processing. Many schemes are available for removing noise
from images. A good denoising scheme retrieves a clearer image even if the
image is highly affected by noise. Image denoising may either be linear or
non-linear. A mean filter is an example of linear filtering, and a median
filter is an example of non-linear filtering. While the linear model has
traditionally been preferred for image denoising because of its speed, the
limitation of this model is that it does not preserve the edges of the image.
The non-linear model preserves the edges well compared to the linear model,
but it is relatively slow. Despite the slowness, non-linear filtering is a
good alternative to linear filtering because it effectively suppresses impulse
noise while preserving the edge information. The median filter ensures that
each pixel in the image fits in with the pixels around it. It filters out
samples that are not representative of their surroundings—the impulses.
Therefore, it is very useful in filtering out missing or damaged pixels. For
2D images, standard median operation is implemented by sliding a window over
the image. The 3 × 3 window size, considered to be effective for the most
commonly used images, is implemented in the IP. At each position of the
window, the nine pixel values inside the window are copied and sorted. The
value of the central pixel is replaced with the median value of the nine
pixels in the window. The window slides right by one column after every clock
cycle until the end of the line. The following illustration shows the effect
of a median-based denoising filter on a noisy image.
Image De-Noising Filter Hardware Implementation
Microsemi Image De-noising Filter IP core—a part of Microsemi’s imaging and
video solutions IP suite—supports 3 × 3 2D median filtering and effectively
removes impulse noise from images.
The Image De-noising Filter hardware contains three one-line buffers storing
one horizontal video line each. The incoming data stream fills these three
buffers, one by one. In the design illustrated in this document, the median
filter is implemented on 3 × 3 matrix, so three lines of video form the 3 × 3
window for the median. When the third buffer contains three pixel values, the
read process is initiated. Three shift registers form the 3 × 3 2D array for
median calculation. These shift registers are applied as input to the median
finder, which contains 8-bit comparators that sort the nine input values in
increasing order of magnitude and produce the median value, which is then
updated into the output register. The new pixel column is shifted into the
shift register, with the oldest data being shifted out. The 3 × 3 window moves
from the left to right and from top to bottom for each frame. The following
illustration shows the block diagram of the Image De-noising Filter hardware
with default RGB888 input.
Inputs and Outputs
The following table lists the input and output ports of the Image De-noising Filter.
Table 1 Image De-Noising Filter Ports
| Port Name | Direction | Width | Description |
|---|---|---|---|
| RESETN_I | Input | Active-low asynchronous reset signal to design | |
| SYS_CLK_I | Input | System clock | |
| R_I | Input | [(g_DATAWIDTH–1):0] | Data input – Red Pixel |
| G_I | Input | [(g_DATAWIDTH–1):0] | Data input – Green Pixel |
| B_I | Input | [(g_DATAWIDTH–1):0] | Data input – Blue Pixel |
| DATA_VALID_I | Input | Input data valid signal | |
| R_O | Output | [(g_DATAWIDTH-1):0] | Data output – Red Pixel |
| G_O | Output | [(g_DATAWIDTH-1):0] | Data output – Green Pixel |
| B_O | Output | [(g_DATAWIDTH-1):0] | Data output – Blue Pixel |
| DATA_VALID_O | Output | Output data valid signal |
Configuration Parameters
The following table lists the configuration parameters for the Image De-
noising Filter design.
Table 2 • Design Configuration Parameters
| Name | Description | Default |
|---|---|---|
| G_DATA_WIDTH | Data bit width | 8 |
| G_RAM_SIZE | Buffer size of RAM | 2048 (for horizontal resolution of 1920) |
Note: These are generic parameters that vary based on the application requirements.
Testbench
To demonstrate the functionality of the Image De-Noise Filter core, a sample
testbench file (imagedenoise_ test) is available in the Stimulus Hierarchy
(View > Windows > Stimulus Hierarchy), and a sample testbench input image file
(RGB_input.txt) is available in the Libero® SoC Files window (View > Windows >
Files). The following table lists the testbench parameters that can be
configured according to the application, if necessary.
Table 3 • Testbench Configuration Parameters
| Resource | Utilization |
|---|---|
| DFFs | 1961 |
| 4_input LUTs | 2417 |
| MACC | 0 |
| RAM1Kx18 | 15 |
| RAM64x18 | 0 |
The following steps describe how to simulate the core using the testbench.
- In the Libero SoC Design Flow window, expand Create Design, and double-click Create SmartDesign Testbench, as shown in the following figure.
Figure 3 • Create SmartDesign Testbench
- Enter a name for the SmartDesign testbench and click OK.
Figure 4 • Create New SmartDesign Testbench Dialog Box
A
SmartDesign testbench is created, and a canvas appears to the right of the
Design Flow pane.
- In the Libero SoC Catalog (View > Windows > Catalog), expand Solutions-Video, and drag the Image De-Noise Filter IP core onto the SmartDesign testbench canvas.
Image De-Noising Filter Hardware Implementation
Figure 5 • Image De-Noise Filter Core in Libero SoC Catalog
The core appears on the canvas, as shown in the following figure.
Figure 6 • Image De-Noise Filter Core on SmartDesign Testbench Canvas
- Select all the ports of the core, right-click, and click Promote to Top Level, as shown in the following figure.
Figure 7 • Promote to Top Level Option
The ports are promoted to the top level, as shown in the following figure.
Figure 8 • Image De-Noise Filter Core Ports Promoted to Top Level
- To generate the Image De-noising Filter SmartDesign component, click Generate Component icon on the SmartDesign Toolbar, as shown in the following figure.
Figure 9 • Generate Component Icon
A sample testbench input image file is created at: …\Project_name\component\Microsemi\SolutionCore\Image_Denoising_Filter\ 1.2.0\Stimulus
- In the Libero SoC Files window, right-click the simulation directory, and click Import Files…, as shown in the following figure.
Figure 10 • Import Files Option
- Do one of the following:
- To import the sample testbench input image, browse to the sample testbench input image file, and click Open, as shown in the following figure.
- To import a different image, browse to the desired image file, and click Open.
Figure 11 • Input Image File Selection
The input image file appears in the simulation directory, as shown in the following figure.
Figure 12 • Input Image File in Simulation Directory
- In the Stimulus Hierarchy, expand Work, and right-click the Image De-noising Filter testbench file (image_denoise_test.v).
- Click Simulate Pre-Synth Design, and then click Open Interactively.
Figure 13 • Open Interactively Option
The ModelSim tool appears with the testbench file loaded on to it, as shown in the following figure.
Figure 14 • ModelSim Tool with Image De-Noising Filter Testbench File
- If the simulation is interrupted because of the runtime limit in the DO file, use the run -all command to complete the simulation. After the simulation is completed, the testbench output image file (.txt) appears in the simulation folder.
Simulation Results
The following illustration shows the effect of the Image De-noising Filter on
a noisy image.
Figure 16 • Image De-Noising Filter Effect on a Noisy Image 2
Resource Utilization
In this design, the Image De-noising Filter is implemented on an MPF300TS-
1FCG1152I PolarFire System-on-Chip (SoC) FPGA. The following table provides
resource utilization data for a 24-bit data width design after synthesis.
Note: Image De-noising Filter supports for SmartFusion2 and PolarFire FPGAs.
Table 4 • Resource Utilization
| Resource | Utilization |
|---|---|
| DFFs | 1961 |
| 4_input LUTs | 2417 |
| MACC | 0 |
| RAM1Kx18 | 15 |
| RAM64x18 | 0 |
Microsemi Proprietary and Confidential UG0643 User Guide Revision 3.0